code: def generate_merged_ids(
      input_ids: torch.tensor,
      target_ids: torch.tensor,
      tokenizer: PreTrainedTokenizer,
      max_sequence_length: int = None,
  ):
      """Generate merged input and target IDs tensor.
  
      This function merges the input_ids and target_ids together to create a unified tensor
      to pass into the model. It also returns attention masks for the merged tensors.
  
      Args:
          input_ids (torch.Tensor): The input IDs tensor.
          target_ids (torch.Tensor or None): The target IDs tensor or None.
          max_sequence_length (int or None): The maximum sequence length to pad or truncate to.
          tokenizer (PreTrainedTokenizer): The tokenizer used to encode the input_ids and target_ids.
  
      Returns:
          torch.Tensor: The merged input and target IDs tensor.
          torch.Tensor: The attention masks for the merged tensor.
      """
      merged_input_and_targets = []
      lengths = []
  
      pad_tensor = torch.tensor([tokenizer.pad_token_id]).to(
          target_ids[0].device
      )
  
      # Merge input_ids and target_ids by concatenating them together.
      # We remove the left padding from both input_ids and target_ids before concatenating them.
      for input_id_sample, target_id_sample in zip(
          input_ids, target_ids
      ):
          input_id_sample_no_padding = remove_left_padding(
              input_id_sample, tokenizer
          )[0]
          target_id_sample_no_padding = remove_left_padding(
              target_id_sample, tokenizer
          )[0]
          target_id_sample_no_padding = torch.cat(
              (target_id_sample_no_padding, pad_tensor), dim=-1
          )
  
          merged_sample_ids = torch.cat(
              (input_id_sample_no_padding, target_id_sample_no_padding),
              dim=-1,
          )
          # If the merged tensor is longer than the maximum sequence length, we truncate it.
          if (
              max_sequence_length
              and merged_sample_ids.shape[0] > max_sequence_length
          ):
              merged_sample_ids = merged_sample_ids[
                  :max_sequence_length
              ]
  
          merged_input_and_targets.append(merged_sample_ids)
          lengths.append(merged_sample_ids.shape[0])
  
      # Since we remove the left padding from the target_ids, the merged input_ids and target_ids
      # may not have the same lengths. We need to align them to the same length by adding left padding
      # and generate an attention mask for just the part of the input that is not padding.
      max_length = max(lengths)
      attention_masks = []
      for i, merged_sample_ids in enumerate(merged_input_and_targets):
          merged_input_and_targets[i] = add_left_padding(
              merged_sample_ids, max_length
          )
          attention_masks.append(
              create_attention_mask(
                  merged_input_and_targets[i], tokenizer
              )
          )
  
      return torch.stack(merged_input_and_targets), torch.stack(
          attention_masks
      )
  
imports: import copy
  import logging
  import tempfile
  from typing import Dict, Optional, Tuple, TYPE_CHECKING, Union
  import torch
  import torch.nn.functional
  import transformers
  from bitsandbytes.nn.modules import Embedding
  from packaging import version
  from transformers import AutoConfig, AutoModelForCausalLM, PreTrainedModel, PreTrainedTokenizer, TextStreamer
  from ludwig.constants import IGNORE_INDEX_TOKEN_ID, LOGITS, PREDICTIONS, PROBABILITIES
  from ludwig.schema.trainer import LLMTrainerConfig
  from ludwig.utils.error_handling_utils import default_retry
  from ludwig.utils.logging_utils import log_once
  from ludwig.utils.model_utils import find_embedding_layer_with_path
label_desc: Use image node_id 11 for calling a global function with example usage: generate_merged_ids(input_ids, target_ids, tokenizer, max_sequence_length) and returns: torch,  torch